Time series are data observed over time (either in continuous time or at discrete time periods).

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Retrain Time Series Models

I'm new to TS modeling, but have some experience in classic classification modeling. In classification I can train one model and use it for some time while some indices are stable (e.g. PSI). ...
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Distinguishing diffusion from white noise

I have a time series that looks like this: This comes from an experiment, and I know the following: Originally, for $t < s$ the time series is $x_t = vt + e_i$, where $v$ for this particular ...
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102 views

Multiple testing in correlation analysis over time periods

I have one variable measured once per time interval (say, once per year), and another variable measured periodically (say, once per day). The periodic measurements are autocorrelated. I am interest ...
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31 views

Generalized likelihood ratio test

Does anyone use the generalized likelihood ratio test for detecting a sudden change in time series forecasting (ARIMA Model)? A paper by Bonne Zhu uses this technique for anomaly detection, but I ...
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48 views

Multilinear Model with fixed intercept

I would like to fit the following model Y (t) = m (t) + b * t + g * C (t) + N (t) with m (t) to be the long term mean monthly values (remove seasonal component), b ...
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30 views

How to transform non-Gaussian multivariate time series

I wish to apply a VAR-like kind of model to a multivariate time series dataset. The model assumes that $X_t | X_{t-1} \sim \mathcal{N}(\Gamma X_{t-1},\Omega)$ for $X_t \in \mathbb{R}^p$. I want to ...
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28 views

comparison of forecasting models for daily data (frequecy=365)

I have 852 days of daily attendance data and need to use the first 800 days data to predict the next 52 days and match it with my actual values. How do i decide which is the best model to use for ...
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24 views

Formatting Time-series data for Cross-correlation

In an experiment we measured 100 response times for each subject. The data has the following format: ...
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30 views

combining subjective probability estimates and statistical estimates for forecasting

At the end of the year forecasters usually struggle year to predict landing estimate for the financial year due to variety of reasons including volatility, unreliable demand projections, inventory ...
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Hypothesis testing on groups of time series data

I have some experimental data from two groups, where each group contains data from $n$ subjects. The data are in the form of a time series for each subject, but are not all the same length. To be ...
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28 views

Avoiding spurious regression with cross-sectional data

I have been reading everything I can get my hands on about spurious regression but can't seem to definitively find out what is best in regressing cross-sectional data including non-stationary ...
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28 views

Average time series data (summarize) increasing slope of line

We are working on time series data forecasting. Our input data set is large, so thought to average two consecutive data points and reduce it to half the size. But we have observed that average values ...
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38 views

Formula behind forecast in R

Can anyone tell me the formula behind the forecast function in R? Preferably in the form easily understood by mathematicians (e.g x_t, θ etc) Here is my code in ...
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Normalising two time series for correlation analysis

this is my very first time posting in CV and I am very new to analysis area. I really really need you experts' help. I have been working on two time series data (currency spread differentiated by ...
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23 views

How many lags for johansen and vecm model?

I want to use VECM model for return as dependent variable and I have 5 variables as independent; my data is monthly ...and my questions are: How could I know if I should use linear or non linear for ...
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22 views

Explaining the methodology behind this ARIMA weighted code

I have a code that was given to me that runs an ARIMA model putting weight on more recent errors, it gives excellent results, much better than simple ARIMA, but i do not understand the methodology ...
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101 views

What is PCA doing with autocorrelated data?

Just because some correspondent posed an interesting question concerning methods of computation of autocorrelation, I began to play with it, nearly without any knowledge about time series and ...
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59 views

Problem on time-series

I am dealing with event data (recorded over a month) which gives out a binary response from a sensor when a door opens or closes - the time is noted at every instant and can also be represented in ...
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28 views

simple exponential smoothing with drift

I have researched all over the text books and software (R/SAS/SPSS), but I have not encountered Simple Exponential Smoothing (SES) with a drift ? Is it possible to add a drift term to Simple ...
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44 views

Auto correlation function of AR(p) process

I am doing a time series course and in the theory part there are few things I don't understand.In obtaining auto correlation function for AR(p) process it is done as: AR(p)=$X_t = α_1X_{t−1} + ...
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47 views

Understanding / Interpreting VARselect function in R

Atm I am playing around with VAR-Models and I was asking myself how to properly use the VARselect function. My question is the following: What should I give R as y? In the Help it just states "Data ...
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92 views

PDF and CDF of sum of random variables with different distributions

This may sound too trivial but I am having difficulty to solve an assignment problem where I need to determine the distribution and density of a random variable $Z$ which is the sum of random ...
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12 views

Clustering cases on variables discovered in-sample via factor analysis?

My Data I have 2-hourly readings on approximately 10K sensors taken over the course of a year. The resulting time series look pretty similar day to day (though there are some longer term trends), and ...
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27 views

Time series: What is my natural time period?

We are modeling univariate ts with R. Sampled daily since 1-1-2013 at five observations per week. We are unclear about how to decide 'natural time period'. Until now we just assumed 260 weekdays in ...
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Modeling an I(1) process with a cointegrating I(1) and an I(0) variable

A colleague says that estimating the following time series model is statistically sound: $$y_t = \beta_0 + \beta_1 x_{1t} + \beta_2 x_{2t} + e_t$$ where $y_t$ is nonstationary $I(1)$, $x_{1t}$ is ...
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Interpretation of results for unitroot test

Let's say I have a pure random walk: ...
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34 views

How to estimate model where instrument is correlated with dependent variable

I have the following problem: I would like to estimate the effect of price variation caused by uncertainty on an outcome variable. P is my price, X is the variable measuring uncertainty and Y is the ...
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How to prove the siginicance difference level in this data?

I have collected a set of data of 52 weeks of actual output and demand. Actual 1100 1300 1400 1500 1600 1100 1200 1600 2100 1300 1600 1300 1600 2200 2300 1700 1800 800 1400 900 2100 1400 1800 1900 ...
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49 views

Outliers In Predicted Intervals

In my stats class today, the professor was showing us some output from MINITAB on a prediction interval that was calculated (from time series data). For one of the prediction intervals, MINITAB had an ...
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8 views

Characterizing “typical behavior” for events?

I need to build a model to characterize what is typical for a series of events, which in turn will be used to flag atypical events. As an example, think of credit card purchases (how often? what ...
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20 views

R implementation of Zeger's parameter-driven (latent process) approach to time series regression with count data

For time series regressions with count data, Poisson-response with log link (i.e. GLM) is widely used. However, such models often suffer from serial correlation. One approach to handle was introduced ...
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20 views

Create a damping function for discrete time series data such that values converge to constant value

I have an agent-based model where an agent predicts output and then compares that value to the actual output. How can I create a damping function of sorts that will cause the delta between expected ...
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40 views

fitting garch (1.1) model in r or eviews

How I can have positive GARCH (1.1) parameters value using "R" or "eviews" by taking a dummy variable. Following are my data and how the dummy looks like. So I need to calculate GARCH (1.1) parameters ...
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Auto covariance of an AR model

I have an AR(2) series: $X_t = 1.5*X_{(t-1)} - 1.2 * X_{(t-2)}-3*\epsilon_{t}$. Given $X_0 = 0, X_1$ ~ N(0,1). Then Cov($X_2, X_3)$ = $Cov(1.5*X_1 -3*\epsilon_2, 1.5*(1.5*X_1 -3*\epsilon_2)-1.2*X_1 ...
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Arima time series forecast (auto.arima) with multiple exogeneous variables in R

I would like to conduct a forecast based on a multiple time series ARIMA-model with multiple exogeneous variables. Since I am not that skillfull with regards to neither statistics nor R I want to keep ...
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What's the difference between time-series econometrics and panel data econometrics?

This question may be very naive, but the way I'm taught econometrics I'm very confused if there's a difference between time-series and panel data method. Regarding time series, I've covered topics ...
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64 views

time series forecasting with 53 weeks in a year

I am building a time series model in R. I have four years of data from 2010 to 2013 and doing forecasting fro 2014. According to the calendar that my organisation follows, In 2014 , there would be 53 ...
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OLS versus ML estimation of VECM

A vector error correction (VECM) model has an equivalent vector autoregression (VAR) representation. (VECM) $\;\;\;\Delta y_t=\Pi y_{t-1}+\Gamma_1\Delta y_{t-1}+...+\Gamma_{p-1}\Delta ...
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Compute frequency of time series

I would like to understand how is the period or frequency of time series calculated. Shouldn't a weekly repeating pattern be of the frequency 7, and the yearly pattern be 365? I ask because the paper ...
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Proportion (difference) test for drifting proportions

Two machines are spitting out coins (at different non-constant rate). The probability of flipping heads for every machine is believed to change very slowly in time (compared to flipping rate). It is ...
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53 views

TBATS: why set seasonal periods?

While trying to estimate the level, trend, and seasonal components with the TBATS model (forecast pkg in R), I notice that the ...
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What is variance and co variance related to time series?

I'm trying to understand the Mahalanobis distance method which makes use of a covariance matrix. However i am not clear about the idea of variance and covariance with respect to time series. And also ...
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54 views

3 month forecast for commodity prices in R - general help for approach

In the following I'll describe my undertaking as detailed as possible in order to provide you enough information. Please keep in mind (when answering) that neither I'm a matematician nor a ...
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55 views

Analyzing time series in depth

I want to create a code which tests absolutely everything for time-series forecasting accuracy. The current tests that I do are: bptest() - tests against ...
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97 views

Weekly seasonality model by ARIMA+Fourier terms+dummies

This is a long post but it is not conceptually difficult. Please bear with me. I am trying to model the seasonality of production volume of an agricultural commodity. I do not care about the ...
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42 views

Methods of fitting a dynamic linear model

I'm taking a time series course and am learning about exchangeable time series form of dynamic linear models (DLMs). This is given by: \begin{align*} \mathbf{y}_t' &= ...
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3answers
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Cointegration - Why can't I estimate a VAR on the differences?

When talking about variables that are I(1) (the first difference is stationary), Lutkepohl book says: "...in general, a VAR process with cointegrated variables does not admit a pure VAR representation ...
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61 views

What are the implications of estimating a covariance matrix from a correlated sample?

Given a sample of $n$ independent observations $x_1,...,x_n$ (where $x_i$ are $p$-dimensional column vectors), the $p \times p$ sample covariance matrix is defined as ...
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Time Series Function - Constant vs Piecewise

I have daily data for online marketing $ spend and the number of clicks to the website gained. I want to determine a function that 'maps' the two together. I cannot use normal linear regression ...
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Fit a VAR model with R

I have a bivariate time series z_t where z_1t is the change in monthly US treasury bills (maturity 3 months) and z_2t the inflation rate,in percentage, of the U.S. monthly consumer price index ...